Exploring non-linear effects of walking accessibility on well-being in rural older adults of Jintang County: a random forest analysis.

Objective: The global concern surrounding the aging population has brought the well-being of older individuals to the forefront of societal attention. Unfortunately, studies focusing on the well-being of older people residing in rural areas are frequently overshadowed by the developmental disparities between rural and urban regions. Thus, this study aims to delve into the non-linear impact of walking accessibility on the subjective well-being of rural older adults. The goal is to gain a comprehensive understanding of this relationship, ultimately contributing to an improved quality of life and health for older adults in rural areas. Methods: In this study, the Random Forest algorithm was employed to explore the non-linear effects of demographic variables, perceived safety, subjective built environment (including perceptions and preferences of the built environment), and walking accessibility on the subjective well-being of older adults. Results: The findings of this study underscore the pivotal role of walking accessibility in influencing the well-being of older adults, particularly in terms of access to bazaars and health centers, where non-linear and threshold effects are evident. Furthermore, community safety, road conditions, and walking preferences were identified as positive influencers on the well-being of older adults. Well-being trends varied with age, revealing noteworthy non-linear relationships for certain variables. Conclusion: The insights gained from this study provide crucial theoretical guidance for the development of policies tailored to the unique context of rural aging. By taking into account factors such as walking accessibility, community safety, health support, and social interaction, we can create an improved living environment for rural older adults, ultimately enhancing their happiness and overall quality of life.

Enhancing prediction of supraspinatus/infraspinatus tendon complex injuries through integration of deep visual features and clinical information: a multicenter two-round assessment study.

OBJECTIVE: Develop and evaluate an ensemble clinical machine learning-deep learning (CML-DL) model integrating deep visual features and clinical data to improve the prediction of supraspinatus/infraspinatus tendon complex (SITC) injuries. METHODS: Patients with suspected SITC injuries were retrospectively recruited from two hospitals, with clinical data and shoulder x-ray radiographs collected. An ensemble CML-DL model was developed for diagnosing normal or insignificant rotator cuff abnormality (NIRCA) and significant rotator cuff tear (SRCT). All patients suspected with SRCT were confirmed by arthroscopy examination. The model's performance was evaluated using sensitivity, specificity, accuracy, and area under the curve (AUC) metrics, and a two-round assessment was conducted to authenticate its clinical applicability. RESULTS: A total of 974 patients were divided into three cohorts: the training cohort (n = 828), the internal validation cohort (n = 89), and the external validation cohort (n = 57). The CML-DL model, which integrates clinical and deep visual features, demonstrated superior performance compared to individual models of either type. The model's sensitivity, specificity, accuracy, and area under curve (95% confidence interval) were 0.880, 0.812, 0.836, and 0.902 (0.858-0.947), respectively. The CML-DL model exhibited higher sensitivity and specificity compared to or on par with the physicians in all validation cohorts. Furthermore, the assistance of the ensemble CML-DL model resulted in a significant improvement in sensitivity for junior physicians in all validation cohorts, without any reduction in specificity. CONCLUSIONS: The ensembled CML-DL model provides a solution to help physicians improve the diagnosis performance of SITC injury, especially for junior physicians with limited expertise. CRITICAL RELEVANCE STATEMENT: The ensembled clinical machine learning-deep learning (CML-DL) model integrating deep visual features and clinical data provides a superior performance in the diagnosis of supraspinatus/infraspinatus tendon complex (SITC) injuries, particularly for junior physicians with limited expertise. KEY POINTS: 1. Integrating clinical and deep visual features improves diagnosing SITC injuries. 2. Ensemble CML-DL model validated for clinical use in two-round assessment. 3. Ensemble model boosts sensitivity in SITC injury diagnosis for junior physicians.

LARAI portal provides a safe method for lateral meniscus repair: three-dimensional computed tomography and cadaveric assessment.

BACKGROUND: Lateral, All-Round and All-Inside (LARAI) portal is a viewing or working portal for observing and repairing the lesions of the lateral meniscus. However, there are safety concerns about popliteal artery (PA) injuries during the procedure. This study aimed to assess the safe distance between the trajectory of the LARAI portal and PA. MATERIALS AND METHODS: Both three-dimensional computed tomography (3D-CT) and cadavers were used to simulate the LARAI portal trajectory. In the 3D-CT study, between January 2020 and September 2020, 45 participants who underwent computed tomography angiography were included in the study. The shortest distance from the PA to the simulated trajectory needle (PS) was measured using 3D-CT. Mean -3SD -2 was calculated to assess the safety of the LARAI portal trajectory. If this value was more than zero, the trajectory was considered "safe." In the cadaveric study, lower limbs from seven fresh-frozen cadavers were used to establish the "safe" trajectories of the LARAI portal, and the PS was measured. RESULTS: In the 3D-CT study, the longest PS (P < 0.001) was found 20 mm lateral to the edge of the patellar tendon trajectory at 0 mm from the posterior cruciate ligament (PCL). Safe trajectories were also found 10 mm, 15 mm, and 20 mm lateral to the edge of the patellar tendon at 0 mm from the PCL, as well as the 20 mm lateral to the edge of the patellar tendon at 3 mm from the PCL. The cadaveric study showed that the average PS of all safe trajectories closely adjoined to PCL was greater than 14 mm. CONCLUSIONS: The LARAI portal trajectory in the "figure of four" is safe, and the optimal insertion point is 10-20 mm lateral to the edge of the patellar tendon and closely adjoined to the posterolateral margin of the PCL at knee joint line level. LEVEL OF EVIDENCE: Level IV.

Validation of a Mobile Version of the American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form: An Observational Randomized Crossover Trial.

BACKGROUND: The American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form (ASES) questionnaire is an effective tool for evaluating shoulder joint function. The development and usage of a mobile version of the ASES questionnaire has the potential to save time, money, and effort. OBJECTIVE: The aim of this study is to assess the equivalence between the paper and mobile versions of the ASES questionnaire and their acceptability among patients. METHODS: The paper and mobile versions of the ASES questionnaire were used to evaluate the shoulder joint function of 50 patients with shoulder pain. This study included patients from the shoulder clinic of Sun Yat-sen Memorial Hospital. The intraclass correlation coefficient (ICC) and Bland-Altman method were used to evaluate the agreement (reliability) of the scores obtained by the two methods (paper versus mobile). RESULTS: Of the 50 patients recruited from March 2018 to May 2019, 46 (92%) completed the study. There was a high agreement between the paper and mobile versions of the ASES questionnaire (ICC=0.979, 95% CI 0.943-0.987; P<.001). The mean difference between the scores of the mobile and paper versions was 1.0, and only 1/46 (2%) had a difference greater than the minimal clinically important difference of 12 points. About 75% of patients preferred the mobile version to the paper version. CONCLUSIONS: Our study shows that the mobile version of the ASES questionnaire is comparable to the paper version, and has a higher patient preference. This could prove to be a useful tool for epidemiological studies and patient follow-up over longer periods of time.

Intelligent identification of multi-level nanopore signatures for accurate detection of cancer biomarkers.

To achieve accurate detection of cancer biomarkers with nanopore sensors, the precise recognition of multi-level current blockage events (signature) is a pivotal problem. However, it remains rather a challenge to identify the multi-level current blockages of target biomarkers in nanopore experiments, especially for the nanopore analysis of serum samples. In this work, we combined a modified DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm with the Viterbi training algorithm of the hidden Markov model (HMM) to achieve intelligent retrieval of multi-level current signatures from microRNA in serum samples. The results showed that the developed intelligent data analysis method is highly efficient for processing the large-scale nanopore data, which facilitates future application of nanopores to the clinical detection of cancer biomarkers.

High-bandwidth nanopore data analysis by using a modified hidden Markov model.

Nanopore-based sensing is an emerging analytical technique with a number of important applications, including single-molecule detection and DNA sequencing. In this paper, we developed a Modified Hidden Markov Model (MHMM) to analyze directly the raw (unfiltered) nanopore current blockade data, which significantly reduced the filtering-induced distortion of the nanopore events. Traditionally, prior to further analysis, the measured nanopore data need to be pre-filtered to supress the strong noises. Nonetheless, this would result in the distortion of the shape of the blockade current especially for rapid translocations and bumping blockades. The HMM has been proved to be robust with respect to highly noisy data and thus ideally suitable for processing raw nanopore data directly. Unfortunately, its performance is somehow sensitive to the initial parameters usually preset arbitrarily. To overcome this problem, we use the Fuzzy c-Means (FCM) algorithm to initialize the HMM parameters automatically. Then we use the Viterbi training algorithm to optimize the HMM. Finally, the application results on both the simulated and experimental data are presented to demonstrate the practicability of the developed method for accurate detection of the nanopore current blockade events. The proposed method enables detection of the nanopore events at the highest bandwidth of the commercial instruments to extract the true useful information about the single molecules under analysis.

Fluorogenic probing of specific recognitions between sugar ligands and glycoprotein receptors on cancer cells by an economic graphene nanocomposite.

Economical nanocomposites based on π-stacking of N-acetyl glycosyl rhodamine B to graphene oxide (GO) are simply prepared. These "sweet" GO-materials are proven to be admirable for the fluorogenic recognition of specific intercellular sugar-based ligand-glycoprotein receptor interactions of interest.

Portable surface-enhanced Raman scattering sensor for rapid detection of aniline and phenol derivatives by on-site electrostatic preconcentration.

A portable surface-enhanced Raman scattering (SERS) sensor is developed and applied to simultaneous detection of aniline and phenol derivatives in a label-free way with an electrostatic preconcentration technique to amplify the signals. A SERS-active substrate, silver-electrodeposited screen-printed electrodes (Ag-SPEs), is used for qualification and quantification of polar organic pollutants. Observation of SERS spectra at different potentials indicates that polar pollutants are selectively adsorbed on the Ag-SPEs at a given potential, suggesting that Ag-SPEs could selectively attract polar pollutants to an oppositely charged electrode at different potentials. Optimum SERS-active substrate was obtained when a potential of -0.15 V vs Ag/AgCl was applied on the SPEs in 0.1 M AgNO(3) solution for 10 min. Moreover, the effects of experimental variables such as the electrodeposition time and potential of Ag and preconcentration time of polar molecules on the SERS signals are presented. Under optimum conditions and with a 785 nm laser, the method is effective over a wide range of concentration (1 nM to 1 µM) for aniline and phenol derivatives. The novel method described herein presents a new detection regime for environmental pollutant analysis and also demonstrates simultaneous multiplexed detection of polar organic pollutants using convenient Ag-SPEs.

[Cost-effectiveness analysis on continuous subcutaneous insulin infusion and multi-point daily insulin injections in the treatment program of type 2 diabetes].

OBJECTIVE: To observe the cost-effectiveness of using continuous subcutaneous insulin infusion (CS II) and multi-point daily insulin injections (MDI) in controlling blood sugar in the newly hospitalized type 2 diabetes patients. METHODS: Retrospective analysis on 86 cases taking CS II and 103 cases using MDI on a 'blood sugar control program' among the newly hospitalized patients with type 2 diabetes. The period for observation was 2 weeks, using cost-effectiveness analysis methods to evaluate the two treatment programs. RESULTS: After two weeks of treatment, the effectiveness in the control of blood sugar in CS II group was similar to the MDI group, with no significant difference (P<0.05) and the adverse reactions were similar. Costs in the CS II program (Yuan/person) was less than in the MDI program (1478.34 vs. 1620.46), with significant differences (P< 0.05). The cost-effectiveness ratios (C/E) were 15.07 in the CS II group, and 16.34 in the MDI group, with no significant difference (P>0.05). In order to further reduce the cost of CS II group as a reference, the incremental cost-effectiveness ratio (DeltaC/DeltaE) of the MDI group was 129.20. CONCLUSION: Costs-effective of the CS II program was better than the MDI one in treating the newly hospitalized patients with type 2 diabetes, suggesting that CS II program might be a better choice for hospitals to carry on an intensive insulin therapy program.